Liling Tan

838 total citations
46 papers, 521 citations indexed

About

Liling Tan is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Liling Tan has authored 46 papers receiving a total of 521 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 6 papers in Molecular Biology and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Liling Tan's work include Natural Language Processing Techniques (30 papers), Topic Modeling (26 papers) and Text Readability and Simplification (8 papers). Liling Tan is often cited by papers focused on Natural Language Processing Techniques (30 papers), Topic Modeling (26 papers) and Text Readability and Simplification (8 papers). Liling Tan collaborates with scholars based in Germany, China and Singapore. Liling Tan's co-authors include Marcos Zampieri, A S Truswell, Josef van Genabith, Nikola Ljubešić, Jörg Tiedemann, Preslav Nakov, Francis Bond, Raymond Hendy Susanto, Shamil Chollampatt and Santanu Pal and has published in prestigious journals such as Construction and Building Materials, Geoderma and Theriogenology.

In The Last Decade

Liling Tan

44 papers receiving 467 citations

Peers

Liling Tan
Comparison fields: 5 of 87
  • Artificial Intelligence 340
  • Nutrition and Dietetics 88
  • Molecular Biology 54
  • Civil and Structural Engineering 39
  • Surgery 38
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Citations per field, relative to Liling Tan
Liling Tan · 1×
Citations per year, relative to Liling Tan
Liling Tan · 1×

Countries citing papers authored by Liling Tan

Since Specialization
Citations

This map shows the geographic impact of Liling Tan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Liling Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liling Tan more than expected).

Fields of papers citing papers by Liling Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liling Tan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Liling Tan. The network helps show where Liling Tan may publish in the future.

Co-authorship network of co-authors of Liling Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Liling Tan. A scholar is included among the top collaborators of Liling Tan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Liling Tan. Liling Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 2
2 1
3 8
4 2
5 3
6 10
7 1
8 10
9 7
10 11
11 2
12
Unconstrained Production Categorization with Sequence-to-Sequence Models.
1
13
Don't Classify, Translate: Multi-Level E-Commerce Product Categorization via Machine Translation
1
14
Faster and Lighter Phrase-based Machine Translation Baseline
1
15 2
16
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)
17
17 2
18 16
19 5
20 1

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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